Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations182936
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.7 MiB
Average record size in memory239.0 B

Variable types

Numeric7
Text1
Categorical4

Alerts

Entite has constant value "Article" Constant
Indice has constant value "" Constant
IDCodeBarre is highly overall correlated with IdEntite and 4 other fieldsHigh correlation
IdEntite is highly overall correlated with IDCodeBarre and 3 other fieldsHigh correlation
NumInterne is highly overall correlated with IDCodeBarre and 3 other fieldsHigh correlation
Prix is highly overall correlated with IDCodeBarre and 2 other fieldsHigh correlation
isSynchronized is highly overall correlated with IDCodeBarre and 2 other fieldsHigh correlation
isSynchronizedWeb is highly overall correlated with IDCodeBarreHigh correlation
isSynchronizedWeb is highly imbalanced (97.5%) Imbalance
IDSerieArticle is highly skewed (γ1 = 75.30310313) Skewed
IDCodeBarre has unique values Unique
CodeBarre has unique values Unique
Prix has 85140 (46.5%) zeros Zeros
NumInterne has 112392 (61.4%) zeros Zeros
IDSerieArticle has 182895 (> 99.9%) zeros Zeros

Reproduction

Analysis started2025-03-09 14:52:24.425643
Analysis finished2025-03-09 14:52:40.767107
Duration16.34 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

IDCodeBarre
Real number (ℝ)

High correlation  Unique 

Distinct182936
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148072.12
Minimum110
Maximum243063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:40.850852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile64644.75
Q1101255.75
median147104.5
Q3194885.25
95-th percentile233753.25
Maximum243063
Range242953
Interquartile range (IQR)93629.5

Descriptive statistics

Standard deviation54522.059
Coefficient of variation (CV)0.36821286
Kurtosis-1.1238732
Mean148072.12
Median Absolute Deviation (MAD)46742
Skewness0.0018802393
Sum2.7087721 × 1010
Variance2.9726549 × 109
MonotonicityStrictly increasing
2025-03-09T15:52:40.982481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
243063 1
 
< 0.1%
110 1
 
< 0.1%
111 1
 
< 0.1%
112 1
 
< 0.1%
113 1
 
< 0.1%
114 1
 
< 0.1%
115 1
 
< 0.1%
116 1
 
< 0.1%
243047 1
 
< 0.1%
243046 1
 
< 0.1%
Other values (182926) 182926
> 99.9%
ValueCountFrequency (%)
110 1
< 0.1%
111 1
< 0.1%
112 1
< 0.1%
113 1
< 0.1%
114 1
< 0.1%
115 1
< 0.1%
116 1
< 0.1%
117 1
< 0.1%
118 1
< 0.1%
119 1
< 0.1%
ValueCountFrequency (%)
243063 1
< 0.1%
243062 1
< 0.1%
243061 1
< 0.1%
243060 1
< 0.1%
243059 1
< 0.1%
243058 1
< 0.1%
243057 1
< 0.1%
243056 1
< 0.1%
243055 1
< 0.1%
243054 1
< 0.1%

CodeBarre
Text

Unique 

Distinct182936
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.8 MiB
2025-03-09T15:52:41.259737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.999902
Min length1

Characters and Unicode

Total characters2378150
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182936 ?
Unique (%)100.0%

Sample

1st row360684800000
2nd row360684800001
3rd row360684800002
4th row360684800003
5th row360684800004
ValueCountFrequency (%)
3606848000089 1
 
< 0.1%
3606848747793 1
 
< 0.1%
360684800000 1
 
< 0.1%
360684800001 1
 
< 0.1%
360684800002 1
 
< 0.1%
360684800003 1
 
< 0.1%
360684800004 1
 
< 0.1%
360684800005 1
 
< 0.1%
3606848747649 1
 
< 0.1%
3606848747656 1
 
< 0.1%
Other values (182926) 182926
> 99.9%
2025-03-09T15:52:41.649728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 500177
21.0%
8 358472
15.1%
0 295316
12.4%
3 293366
12.3%
4 292734
12.3%
7 196013
 
8.2%
5 115975
 
4.9%
2 111933
 
4.7%
1 111348
 
4.7%
9 102814
 
4.3%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2378148
> 99.9%
Control 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 500177
21.0%
8 358472
15.1%
0 295316
12.4%
3 293366
12.3%
4 292734
12.3%
7 196013
 
8.2%
5 115975
 
4.9%
2 111933
 
4.7%
1 111348
 
4.7%
9 102814
 
4.3%
Control
ValueCountFrequency (%)
1
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2378149
> 99.9%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
6 500177
21.0%
8 358472
15.1%
0 295316
12.4%
3 293366
12.3%
4 292734
12.3%
7 196013
 
8.2%
5 115975
 
4.9%
2 111933
 
4.7%
1 111348
 
4.7%
9 102814
 
4.3%
Latin
ValueCountFrequency (%)
c 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2378150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 500177
21.0%
8 358472
15.1%
0 295316
12.4%
3 293366
12.3%
4 292734
12.3%
7 196013
 
8.2%
5 115975
 
4.9%
2 111933
 
4.7%
1 111348
 
4.7%
9 102814
 
4.3%
Other values (2) 2
 
< 0.1%

Entite
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
Article
182936 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1280552
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArticle
2nd rowArticle
3rd rowArticle
4th rowArticle
5th rowArticle

Common Values

ValueCountFrequency (%)
Article 182936
100.0%

Length

2025-03-09T15:52:41.766119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:41.830602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
article 182936
100.0%

Most occurring characters

ValueCountFrequency (%)
A 182936
14.3%
r 182936
14.3%
t 182936
14.3%
i 182936
14.3%
c 182936
14.3%
l 182936
14.3%
e 182936
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1097616
85.7%
Uppercase Letter 182936
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 182936
16.7%
t 182936
16.7%
i 182936
16.7%
c 182936
16.7%
l 182936
16.7%
e 182936
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 182936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1280552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 182936
14.3%
r 182936
14.3%
t 182936
14.3%
i 182936
14.3%
c 182936
14.3%
l 182936
14.3%
e 182936
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1280552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 182936
14.3%
r 182936
14.3%
t 182936
14.3%
i 182936
14.3%
c 182936
14.3%
l 182936
14.3%
e 182936
14.3%

IdEntite
Real number (ℝ)

High correlation 

Distinct22793
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11168.395
Minimum1
Maximum23883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:41.926273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile930
Q14785
median10764
Q317295
95-th percentile22694
Maximum23883
Range23882
Interquartile range (IQR)12510

Descriptive statistics

Standard deviation7134.3072
Coefficient of variation (CV)0.63879429
Kurtosis-1.2494664
Mean11168.395
Median Absolute Deviation (MAD)6187
Skewness0.14030292
Sum2.0431016 × 109
Variance50898339
MonotonicityNot monotonic
2025-03-09T15:52:42.049323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2329 627
 
0.3%
3404 376
 
0.2%
3403 376
 
0.2%
15476 250
 
0.1%
15477 250
 
0.1%
932 99
 
0.1%
1645 80
 
< 0.1%
1125 79
 
< 0.1%
478 78
 
< 0.1%
4856 77
 
< 0.1%
Other values (22783) 180644
98.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 9
< 0.1%
6 22
< 0.1%
7 14
< 0.1%
8 7
 
< 0.1%
9 7
 
< 0.1%
10 7
 
< 0.1%
11 7
 
< 0.1%
12 7
 
< 0.1%
13 7
 
< 0.1%
ValueCountFrequency (%)
23883 7
< 0.1%
23882 7
< 0.1%
23881 8
< 0.1%
23880 1
 
< 0.1%
23879 2
 
< 0.1%
23878 2
 
< 0.1%
23877 2
 
< 0.1%
23876 1
 
< 0.1%
23875 1
 
< 0.1%
23874 1
 
< 0.1%

IdTaille
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.692745
Minimum0
Maximum53
Zeros43
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:42.166965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q313
95-th percentile16
Maximum53
Range53
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1880123
Coefficient of variation (CV)0.59682095
Kurtosis2.5443119
Mean8.692745
Median Absolute Deviation (MAD)4
Skewness0.91566838
Sum1590216
Variance26.915472
MonotonicityNot monotonic
2025-03-09T15:52:42.388048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 15293
 
8.4%
5 15290
 
8.4%
3 15279
 
8.4%
7 15269
 
8.3%
2 15252
 
8.3%
8 14722
 
8.0%
13 11775
 
6.4%
14 11774
 
6.4%
12 11773
 
6.4%
15 10720
 
5.9%
Other values (43) 45789
25.0%
ValueCountFrequency (%)
0 43
 
< 0.1%
1 7942
4.3%
2 15252
8.3%
3 15279
8.4%
5 15290
8.4%
6 15293
8.4%
7 15269
8.3%
8 14722
8.0%
9 10387
5.7%
10 4305
 
2.4%
ValueCountFrequency (%)
53 2
< 0.1%
52 2
< 0.1%
51 2
< 0.1%
50 2
< 0.1%
49 2
< 0.1%
48 2
< 0.1%
47 2
< 0.1%
46 2
< 0.1%
45 2
< 0.1%
44 4
< 0.1%

IDAr_Couleur
Real number (ℝ)

Distinct2494
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean536.60435
Minimum0
Maximum2995
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:42.524586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile122
Q1127
median191
Q3588
95-th percentile2179
Maximum2995
Range2995
Interquartile range (IQR)461

Descriptive statistics

Standard deviation702.61295
Coefficient of variation (CV)1.3093687
Kurtosis3.162452
Mean536.60435
Median Absolute Deviation (MAD)69
Skewness2.0192916
Sum98164253
Variance493664.96
MonotonicityNot monotonic
2025-03-09T15:52:42.649867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 30192
 
16.5%
156 14868
 
8.1%
127 11031
 
6.0%
201 4675
 
2.6%
191 2418
 
1.3%
128 2362
 
1.3%
161 2273
 
1.2%
90 1979
 
1.1%
190 1813
 
1.0%
226 1692
 
0.9%
Other values (2484) 109633
59.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 705
0.4%
2 29
 
< 0.1%
3 53
 
< 0.1%
4 159
 
0.1%
5 28
 
< 0.1%
6 230
 
0.1%
9 1
 
< 0.1%
10 8
 
< 0.1%
11 31
 
< 0.1%
ValueCountFrequency (%)
2995 14
 
< 0.1%
2994 8
 
< 0.1%
2993 56
 
< 0.1%
2992 146
0.1%
2991 1
 
< 0.1%
2990 7
 
< 0.1%
2989 14
 
< 0.1%
2988 28
 
< 0.1%
2987 16
 
< 0.1%
2986 8
 
< 0.1%

Prix
Real number (ℝ)

High correlation  Zeros 

Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.6095
Minimum0
Maximum911
Zeros85140
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:42.814287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.99
Q344.99
95-th percentile84.99
Maximum911
Range911
Interquartile range (IQR)44.99

Descriptive statistics

Standard deviation35.409574
Coefficient of variation (CV)1.2825141
Kurtosis10.854838
Mean27.6095
Median Absolute Deviation (MAD)19.99
Skewness2.0494416
Sum5050771.4
Variance1253.8379
MonotonicityNot monotonic
2025-03-09T15:52:42.950191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85140
46.5%
39.99 10289
 
5.6%
49.99 8684
 
4.7%
29.99 7062
 
3.9%
59.99 6219
 
3.4%
44.99 5625
 
3.1%
69.99 4774
 
2.6%
54.99 4376
 
2.4%
19.99 4367
 
2.4%
79.99 3587
 
2.0%
Other values (154) 42813
23.4%
ValueCountFrequency (%)
0 85140
46.5%
0.01 5
 
< 0.1%
0.1 2
 
< 0.1%
0.2 1
 
< 0.1%
0.25 2
 
< 0.1%
0.35 1
 
< 0.1%
0.5 1
 
< 0.1%
0.75 1
 
< 0.1%
1 1
 
< 0.1%
1.9 1
 
< 0.1%
ValueCountFrequency (%)
911 3
 
< 0.1%
350 5
 
< 0.1%
280 6
 
< 0.1%
249.99 12
 
< 0.1%
199.99 89
 
< 0.1%
190 2
 
< 0.1%
189.99 337
 
0.2%
189 18
 
< 0.1%
179.99 1002
0.5%
179 17
 
< 0.1%

Indice
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
182936 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
182936
100.0%

Length

2025-03-09T15:52:43.052179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:43.108087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumInterne
Real number (ℝ)

High correlation  Zeros 

Distinct70545
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3907392 × 1011
Minimum0
Maximum3.6068487 × 1011
Zeros112392
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:43.190257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.6068483 × 1011
95-th percentile3.6068487 × 1011
Maximum3.6068487 × 1011
Range3.6068487 × 1011
Interquartile range (IQR)3.6068483 × 1011

Descriptive statistics

Standard deviation1.7555759 × 1011
Coefficient of variation (CV)1.2623329
Kurtosis-1.7789811
Mean1.3907392 × 1011
Median Absolute Deviation (MAD)0
Skewness0.47014716
Sum2.5441626 × 1016
Variance3.0820467 × 1022
MonotonicityNot monotonic
2025-03-09T15:52:43.338708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112392
61.4%
3.606848748 × 10111
 
< 0.1%
3.606848747 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
3.606848748 × 10111
 
< 0.1%
Other values (70535) 70535
38.6%
ValueCountFrequency (%)
0 112392
61.4%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
3.606848 × 10111
 
< 0.1%
3.606848 × 10111
 
< 0.1%
ValueCountFrequency (%)
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%
3.606848748 × 10111
< 0.1%

IDSerieArticle
Real number (ℝ)

Skewed  Zeros 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0089539511
Minimum0
Maximum61
Zeros182895
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-09T15:52:43.500572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum61
Range61
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63196845
Coefficient of variation (CV)70.579841
Kurtosis5938.4726
Mean0.0089539511
Median Absolute Deviation (MAD)0
Skewness75.303103
Sum1638
Variance0.39938412
MonotonicityNot monotonic
2025-03-09T15:52:43.797483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 182895
> 99.9%
19 1
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
Other values (32) 32
 
< 0.1%
ValueCountFrequency (%)
0 182895
> 99.9%
1 1
 
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
61 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%

isSynchronized
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
144137 
1
38799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182936
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

Length

2025-03-09T15:52:43.937744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:44.005362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

Most occurring characters

ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182936
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 182936
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 144137
78.8%
1 38799
 
21.2%

isSynchronizedWeb
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
0
182476 
1
 
460

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters182936
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Length

2025-03-09T15:52:44.089623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:44.204011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182936
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 182936
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 182476
99.7%
1 460
 
0.3%

Interactions

2025-03-09T15:52:38.087806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:31.415047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.400083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.514368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.544819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.760880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.852312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:38.295066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:31.542773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.608812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.711324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.764120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.959378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.036840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:38.503910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:31.677179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.761015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.843428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.972376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.089615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.185424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:38.783918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:31.802826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.898472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.993928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.120973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.223223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.331586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:39.101418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:31.944655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.035793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.132736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.246903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.364595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.480329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:39.368751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.060292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.165434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.260113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.392687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.481015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.619954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:39.669297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:32.210782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:33.319065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:34.409689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:35.541573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:36.640358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:37.769834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-09T15:52:44.297007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
IDAr_CouleurIDCodeBarreIDSerieArticleIdEntiteIdTailleNumInternePrixisSynchronizedisSynchronizedWeb
IDAr_Couleur1.0000.0720.0020.158-0.0190.0790.0210.2840.038
IDCodeBarre0.0721.0000.0170.723-0.0530.856-0.7510.6231.000
IDSerieArticle0.0020.0171.0000.015-0.0260.019-0.0150.0280.000
IdEntite0.1580.7230.0151.000-0.0460.714-0.5130.6540.121
IdTaille-0.019-0.053-0.026-0.0461.000-0.062-0.0720.1340.014
NumInterne0.0790.8560.0190.714-0.0621.000-0.7440.6270.062
Prix0.021-0.751-0.015-0.513-0.072-0.7441.0000.0970.008
isSynchronized0.2840.6230.0280.6540.1340.6270.0971.0000.082
isSynchronizedWeb0.0381.0000.0000.1210.0140.0620.0080.0821.000

Missing values

2025-03-09T15:52:40.102158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T15:52:40.435133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDCodeBarreCodeBarreEntiteIdEntiteIdTailleIDAr_CouleurPrixIndiceNumInterneIDSerieArticleisSynchronizedisSynchronizedWeb
0110360684800000Article611190.010011
1111360684800001Article621190.011011
2112360684800002Article631190.012011
3113360684800003Article651190.013011
4114360684800004Article661190.014011
5115360684800005Article671190.015011
6116360684800006Article681190.016011
71173606848000072Article691190.0360684800007011
81183606848000089Article7111560.0360684800008011
91193606848000096Article7121560.0360684800009011
IDCodeBarreCodeBarreEntiteIdEntiteIdTailleIDAr_CouleurPrixIndiceNumInterneIDSerieArticleisSynchronizedisSynchronizedWeb
1829262430543606848747700Article23883161610.0360684874770000
1829272430553606848747717Article23883171610.0360684874771000
1829282430563606848747724Article2375911220.0360684874772010
1829292430573606848747731Article2375921220.0360684874773010
1829302430583606848747748Article2375931220.0360684874774010
1829312430593606848747755Article2375951220.0360684874775010
1829322430603606848747762Article2375961220.0360684874776010
1829332430613606848747779Article2375971220.0360684874777010
1829342430623606848747786Article2375981220.0360684874778010
1829352430633606848747793Article2375991220.0360684874779010